Bibliographic record
Abstract
For more than a decade, toxic and nuisance algal blooms in Lake Erie have increased in frequency, and the summer of 2015 saw the largest documented algal bloom in the lake's history. Blooms threaten drinking water quality, fish populations, beach quality, coastal recreation and the overall health of the lake. In addition, when algae die and decompose, hypoxic conditions can be created, meaning there is a lack of oxygen in the water. In 2012, hypoxic conditions were responsible for tens of thousands of dead fish washing up on a 40-kilometre stretch of shoreline between the communities of Erieau and Port Stanley in Ontario. The increased occurrence of harmful algal blooms in Lake Erie is influenced by many factors, including nutrients, climate change and invasive species such as zebra mussels. Phosphorus is the primary nutrient driving increased algal blooms in the lake and comes from multiple sources, both urban and rural. After decades of work on major sources, non-point sources are now the majority of phosphorus entering Lake Erie. The challenge is significant, but with coordinated action there is hope for the lake's future. However, with the recent rise in blooms, Canada and the United States recognized the need for a new approach to action, and in February 2016 agreed to a 40 per cent reduction target for phosphorus entering the Central and Western basins of Lake Erie. Federal and provincial ministries are collaborating to develop an Action Plan for Lake Erie to Achieve Phosphorus Reductions from Canadian Sources. Governments cannot do this alone. Additional actions from all sectors and communities across the Lake Erie basin are going to be needed to achieve our goals. A draft is currently out for consultation soliciting actions from all sectors. The Ministry of Agriculture, Food and Rural Affairs (OMAFRA) focus is on rural communities and Ontario's agriculture and food systems. Rural and agricultural sources of phosphorus include soil erosion from fields and nutrient runoff from manure, fertilizer and other soil amendments. OMAFRA has been studying the effectiveness of management practices in reducing environmental impacts and promoting environmental planning for decades. To support the work on phosphorus reduction OMAFRA reviewed the available information to determine which practices have the greatest potential for reductions and to identify our scientific gaps. Preliminary conclusions include the need to: improve soil health through practices like crop rotation, reduced tillage, cover crops; carefully manage nutrients including the appropriate timing and application of nutrients; and select practices that are effective in the non-growing season and heavy storm events when the majority of the loss can occur. Current science also indicates that a multi-barrier approach that uses multiple BMPs is most effective at minimizing phosphorus loss from fields through runoff and tile drainage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".